The Best LLMs in 2026: A Plain-English Comparison

The model at the top of the leaderboard may be too expensive for everyday work. The cheapest API may need more attempts to get the answer right.

That leaves a practical question: which models deserve a test on your work? A team reviewing contracts needs different evidence from one building a coding agent or processing thousands of images.

This guide compares the main model families and the costs behind their headline prices. The November 3 snapshot uses Artificial Analysis’s Intelligence Index v5.0.1. Version 5.0 replaced several of the evaluations behind the score, and scores and task costs both moved with it, so do not compare them with figures from earlier index versions. The guide covers recent releases such as Muse Wonder, Muse Wonder Full, Muse Spark Open, Claude Haiku 5.5, Mistral Large 4 (open weights since October 29), GPT-6.1 Sol, GPT-6.1 Terra (released October 26), and Gemini 4 Argon (generally available since October 13), and explains where the free MindsHub Air model and Jev fit.

Where to start testing

• Difficult reasoning and coding: compare Claude Opus 5.5, GPT-6 Astra, and Claude Sonnet 5.5, and include GPT-6.1 Sol, which matches Sonnet 5.5’s score at a fraction of their task cost. Add Muse Wonder when evaluating Meta’s API. Muse Wonder Full rounds to 50, but is available only through qualifying Muse Code subscriptions. Gemini 4 Argon also rounds to 50; it has been generally available since October 13, and its introductory rates run through November 30.
• Everyday work: test GPT-6.1 Sol for coding and multi-step workflows, GPT-6.1 Terra for image-heavy and computer-use work at half Sol’s token rates, and GPT-6 Luna or Claude Haiku 5.5 for focused, high-volume tasks. Compare them at the effort level you would use in production.
• Mixed-media input: Gemini 3.8 Flash accepts text, images, audio, and video. Muse Spark 1.3 and Qwen3.8-Flash-Next are also candidates when you need image or video input.
• Lower-cost text and image work: put MiMo-V2.6-Pro, MiMo-V2.6-Flash, DeepSeek V4.1 Flash, and GLM-5.3-Flash on the shortlist. All four have downloadable, MIT-licensed weights.
• More control over deployment: compare GLM-5.3, Kimi K3, Muse Spark Open, and Mistral Large 4 as well. Check their licences and hardware requirements before choosing to host them yourself.

These are starting points. Run the same tasks through two or three candidates and inspect the results before committing.

Models at a glance

Prices are US dollars per million uncached input and output tokens. The benchmark setting appears beneath each model name. We use the highest published reasoning effort, even where a lower setting scores a point higher, as Claude Fable 5.1’s xhigh row does, or the same for less, as Grok 4.7’s high row does. When a source publishes one reasoning row without an effort label, we say so. Index scores are rounded to whole numbers, so models with the same score can still differ slightly. The benchmark snapshot was checked November 3, 2026. Wonder Full has no public API token rate or published task cost; those cells are left unpriced.

Cost per task is the benchmark’s weighted average token cost. It is neither a spending limit nor the cost of a result your team has accepted. The suggestions in the third column are our assessment of what to test, based on the capabilities and measurements discussed below.

Eleven models from the October 19 table are left out. Artificial Analysis has not scored nine of them on v5.0.1 at the setting that table used, and their earlier scores are not comparable: GPT-5.6 Sol, GPT-5.6 Luna, Claude Opus 5, Claude Fable 5, Claude Sonnet 5, Grok 4.6, Kimi K3, DeepSeek V4.1 Flash, and Qwen3.8-Flash-Next. It now lists all but the last as deprecated. The other two, GPT-6 Sol and GPT-5.6 Terra, now have v5.0.1 scores, but Artificial Analysis lists both as deprecated, and the table includes their successors, GPT-6.1 Sol and GPT-6.1 Terra.

For Muse Wonder Full, N/A means no published API rate or benchmark task cost. It does not mean free usage.

Pricing and access details

The token rates shown are those recorded by Artificial Analysis, checked November 3, 2026. They are developer API rates; consumer subscriptions and MindsHub’s own rate card are separate.

• OpenAI: the list prices in the table apply to prompts up to 272K input tokens. Above that, the whole request costs twice the input rate and 1.5 times the output rate (OpenAI pricing). GPT-6.1 Sol also has an Ultrafast mode, added October 8, that streams faster at six times the standard rates. Check caching terms and tool fees as well.
• Anthropic: Claude Haiku 5.5’s $0.10 / $0.50 applies to prompts up to 100,000 tokens. A longer prompt pays $0.50 / $2.50 on the whole request, and cached tokens count toward that threshold (Anthropic pricing). On October 7, Anthropic halved Claude Sonnet 5.5’s cache-read price to $0.10 per million tokens without changing its input or output rates.
• Grok: long prompts can cost more. Check the provider’s current context thresholds, caching terms, and tool fees.
• Gemini: Google calls the 3.8 Flash rate introductory pricing through December 31, 2026, and lists $1.50 input / $7.50 output from January 1, 2027. For Gemini 4 Argon, Google charges an introductory $2 / $10 through November 30, 2026, and $4 / $20 from December 1 (Gemini API pricing). The table shows the $4 / $20 rates, at which Artificial Analysis costs Argon’s benchmark task at $7.68; at the introductory rates the same run would cost half as much, about $3.84.
• Muse: Wonder and Spark 1.3 use Standard pricing. Spark Open uses the hosted endpoint rates recorded by Artificial Analysis, not a self-hosting cost. Wonder Full has no public API rate. Contributor has different data-use terms and is discussed below.
• Mistral: Large 4’s $0.70 / $2.10 are the rates on its model card, where cached input costs $0.07 per million tokens.
• DeepSeek: the V4 Pro 0813 row uses peak rates. DeepSeek also has off-peak pricing, and cached input has a separate rate. Check the current rate card for the applicable hours and rates.
• Open weights: download access does not promise a particular licence, hosting price, or fit on your hardware. Review the exact checkpoint and host.

This article covers a wider set of models than MindsHub serves. Use the MindsHub model board to check availability and pricing for MindsHub billing.

What each model family offers

OpenAI: Astra, Sol, Terra, and Luna

GPT-6 Astra is OpenAI’s model for its hardest work across code, browsers, and professional files. At max effort it scores 57 in this snapshot, two points behind Claude Opus 5.5, at $6.24 per benchmark task against Opus 5.5’s $10.04. That makes it a useful candidate for complicated workflows with several tools and review steps.

GPT-6 Luna extends that family at a much lower token price: $0.10 input / $0.50 output per million tokens, down from $0.20 / $1.20 for GPT-5.6 Luna. It accepts text and image input and is worth testing for focused tasks at volume.

At max effort, Artificial Analysis scores Luna 28 at $0.15 per benchmark task. It has no v5.0.1 score for GPT-5.6 Sol or GPT-5.6 Luna, so this snapshot cannot compare either with its successor on one scale. It does score two deprecated versions: GPT-6 Sol at 44 for $2.47 per task, against GPT-6.1 Sol’s 55 for $1.27, and GPT-5.6 Terra at 32 for $3.73, against GPT-6.1 Terra’s 51 for $0.96.

GPT-6.1 Sol, which OpenAI released in its API as gpt-6.1-sol on September 29, lists the same $2 input / $10 output rates as GPT-6 Sol for prompts up to 272K input tokens, with cached input at $0.10 instead of $0.20 per million. Artificial Analysis scores it 55 at $1.27 per task at max effort. That is two points below Astra at about a fifth of Astra’s task cost, and it matches Claude Sonnet 5.5’s score at about an eighth of Sonnet’s. Cost per task reflects cached-input pricing and the tokens each model uses, not only the list rates, so confirm the saving on your own workload.

GPT-6.1 Terra, released in OpenAI’s API on October 26, is the same generation’s mid-tier model, which OpenAI describes as fast and efficient for visual reasoning, computer use, and writing. It lists $1 input / $5 output per million tokens for prompts up to 272K input tokens, half GPT-6.1 Sol’s rates. Artificial Analysis scores it 51 at $0.96 per task at max effort: four points below Sol, at about three-quarters of Sol’s task cost.

Effort is part of the choice. Astra’s xhigh and high rows both score 55, at $3.66 and $3.24 per task, against 57 and $6.24 at max. GPT-6.1 Sol’s xhigh row scores 52 at $0.76, against 55 and $1.27, and GPT-6.1 Terra’s scores 45 at $0.40, against 51 and $0.96. Compare the settings on your own evaluation set before paying for the extra reasoning.

Claude: strong results, and effort decides the cost

Claude Opus 5.5 leads this snapshot at 59 and $10.04 per benchmark task at max effort with the default fallback. Its xhigh row scores 57 at $6.05, and high scores 52 at $3.46. Those measurements make effort a practical part of the choice, especially for recurring code and document work.

Claude Sonnet 5.5 scores 55 at $10.02 per task at max with fallback. Its $2 / $10 token rates are half Opus 5.5’s, yet its measured task cost is about the same as Opus 5.5’s $10.04. At max it writes about 285k output tokens per task, against about 173k for Opus 5.5. Xhigh scores 50 at $4.09; high scores 42 at $1.64. Choose the setting as carefully as the model.

Claude Haiku 5.5, released October 7, is Anthropic’s small model for high-volume work and the first Haiku with an adjustable effort setting. It accepts text and image input with a one-million-token context window. At max effort, Artificial Analysis scores it 36 at $0.41 per task. It lists the same $0.10 / $0.50 rates as GPT-6 Luna and scores eight points higher, but costs about 2.7 times as much per task. With identical rates, the difference is token use: Haiku 5.5 writes about three times as many output tokens. Its xhigh row scores 35 at $0.25, and high scores 30 at $0.16. At max, its median wait for the first answer token is about 4.7 minutes, so test lower settings for anything a person waits on.

Claude Fable 5.1 scores 49 at $12.99 per task at max, the highest task cost in the table, and its xhigh row scores a point higher, 50, for $10.85. Artificial Analysis has no v5.0.1 score for Opus 5, Fable 5, or Sonnet 5, so they are not in the table. Opus 5.5, Sonnet 5.5, and Fable 5.1 measurements include a default fallback; Artificial Analysis explains the Fable setup in an article that uses an earlier index version, so its scores differ from the table. Treat each result as a measurement of the named configuration.

Gemini: Flash for mixed input, Argon at an introductory price

Gemini 3.8 Flash accepts text, images, audio, and video. That range is useful when a workflow combines recordings, screenshots, and documents. Its high-effort row scores 31 at $7.77 per benchmark task. Cache writes account for $7.03 of that, which makes caching behavior part of the evaluation.

Gemini 3.8 Flash’s broad score is below the leaders, so check the tasks that matter to you. Google also notes that difficult work can use more tokens and tool calls than with 3.7 Flash. Include those calls in the evaluation, along with the introductory pricing deadline.

Gemini 4 Argon has been generally available since October 13. Developers can call it through the Gemini API in AI Studio and on the Gemini platform; Google AI Ultra subscribers can also use it in Antigravity, and Ultra and Pro subscribers in the Gemini app.

Artificial Analysis lists a single Argon row, high, which it calls the highest effort available. It scores 50 on index v5.0.1, the same rounded score as Muse Wonder Full (50.41 against 50.08 before rounding), above Claude Fable 5.1 and below Opus 5.5, Astra, GPT-6.1 Sol, Sonnet 5.5, and GPT-6.1 Terra. Artificial Analysis costs it at the $4 / $20 that applies from December 1: $7.68 per task, more than Astra’s $6.24, because Argon averages about 116k output tokens per task against about 52k for Astra at max. At the introductory $2 / $10, the same run would cost half as much, about $3.84. Google describes image and long-video understanding but has not published a list of input types.

Google’s own launch table, which uses benchmarks it chose and many results it computed itself, shows Argon leading outright on 13 of its 19 rows and tying one, against Astra, Fable 5.1, and Opus 5.5. On Artificial Analysis’s independent index, Argon sits behind Opus 5.5 and Astra and ahead of Fable 5.1.

Muse: distinguish Wonder, Wonder Full, and Spark Open

Muse Wonder is Meta’s new API model for complex coding and long agent workflows. It accepts text, images, audio, and video, with a roughly one-million-token context window. At max effort, Artificial Analysis scores it 44 at $6.95 per task on $4 / $20 token rates. GPT-6.1 Sol scores 55 for under a fifth of that task cost, so check that Wonder does better on your own tasks before paying more.

Muse Wonder Full is a separate, higher-capability variant. Its max-effort score rounds to 50, level with Gemini 4 Argon and well above Wonder’s 44, but Artificial Analysis publishes no task cost. Meta offers Full only in Muse Code’s Power Usage and Maximum Usage subscriptions; choosing max effort on Wonder’s API does not select Full.

Muse Spark 1.3 remains a closed API model, scoring 37 at $2.39 per task at max on Standard pricing. Use text, images, and video; Meta warns that its audio support is not yet complete and recommends Spark 1.2 for audio.

Muse Spark Open, released October 15, is a distinct downloadable checkpoint, not Spark 1.3’s benchmark result with a different licence. Artificial Analysis scores its xhigh configuration 25 at $2.02 per task using $1.25 / $4.25 reference endpoint rates. It accepts text, images, audio, and video, and has 816 billion total parameters with 42 billion active. Review the Muse Model License and hardware requirements before self-hosting.

Meta’s Contributor option charges $0.10 per million input tokens and $0.20 per million output tokens for supported Spark variants in exchange for permission to use submitted prompts and completions in training. It is unavailable for Wonder’s API and does not support max effort on Spark 1.3. The Standard benchmark figures here do not establish Contributor task costs.

Grok: search access and an effort setting to test

Grok 4.7 adds a newer candidate for long coding and knowledge-work tasks. It accepts text and images, with a 500k context window and access to the provider’s web and X search tools. Search can supply current information; the model still needs to check and use it correctly.

At xhigh, Grok 4.7 scores 35 and costs $7.77 per benchmark task. High scores the same, 35, for $6.18, so the extra reasoning does not pay off on this benchmark. Artificial Analysis has no v5.0.1 score for Grok 4.6, so the table no longer compares the two generations. The table uses the standard 4.7 API, not its separately priced fast variant.

GLM and Kimi: downloadable models with different costs

GLM-5.3 is a text model with a score of 32 and a task cost of $5.01. GLM-5.3-Flash adds image input and uses an MIT licence, and scores 28 at $0.58 per task. The larger model’s custom licence and higher token rates deserve separate consideration.

Kimi K3 supports image input and long coding workflows. Artificial Analysis now lists it as deprecated and has no v5.0.1 score for it at max effort. Downloadable weights give you a hosting option, but its custom licence and hardware needs still determine whether that option works for your team.

MiMo: two low-cost multimodal candidates

MiMo-V2.6-Pro scores 30 at $0.26 per benchmark task, using the API rates recorded by Artificial Analysis: $0.435 input / $0.87 output per million tokens. It is worth testing when cost matters, though one broad score cannot show whether it handles your tasks reliably.

Xiaomi’s Pro-RL model card lists text, image, audio, and video input, a one-million-token context window, and MIT-licensed weights. With 1.02 trillion total parameters, it needs substantial hardware to self-host.

MiMo-V2.6-Flash scores 27 at $0.13 per task on $0.14 input / $0.28 output rates, the lowest task cost in the table. Artificial Analysis calls it slower than average and very verbose, so the low cost comes from the low token rates rather than short answers. Xiaomi’s Flash-RL model card lists text, image, audio, and video input, a one-million-token context window, and MIT-licensed weights, with 309 billion total parameters and 15 billion active.

Qwen: distinguish Max from Flash-Next

Qwen3.8-Max (0902) is a proprietary multimodal API. Qwen3.8-Flash-Next has open weights, text, image, and video input, and a 256k context window.

The current 0902 Max release scores 34 at $10.02 per task. Artificial Analysis has not scored Flash-Next on v5.0.1 yet. The older 0803 Max release has different measurements, so record the release as well as the model name. Context windows, supported input types, and task behavior also differ. The downloadable Qwen3.8 2.4T A95B checkpoint is another distinct model; it is not the Max API.

DeepSeek: V4.1 Flash replaces the older Flash API

DeepSeek V4.1 Flash launched on September 10 with native text and image input, a one-million-token context window, and MIT-licensed weights. Artificial Analysis now lists this September checkpoint as deprecated and has no v5.0.1 score for it; it lists the October 17 release separately.

DeepSeek’s current API documentation names the new model deepseek-flash. The older V4 Flash and Flash Vision API names now route to V4.1 Flash. V4 Pro remains available: the current documentation withdraws the earlier plan to replace it after September 14 and says its billing will stay unchanged. Check the model behind an alias when comparing older results.

The name Flash does not mean it will run on a laptop. Its model card lists 552 billion backbone parameters plus a 196-billion-parameter Engram memory component; self-hosting needs a suitable serving setup and hardware budget.

Mistral: Large 4 with open weights

Mistral released the weights of Mistral Large 4 on October 29, after three more weeks of reinforcement learning since its October 6 public preview. Mistral calls it its largest model: a mixture-of-experts with about 1 trillion total parameters and 52 billion active per token, and it reads images as well as text. Its model card lists a one-million-token context window.

Artificial Analysis scores the release 26 at high effort, the default, at $2.06 per task on Mistral’s $0.70 / $2.10 list rates. The weights come under the Mistral Ecosystem Open-Weight License, a modified MIT licence that allows commercial and non-commercial use; running a model-as-a-service business on it needs a separate commercial licence from Mistral. Self-hosting a model this size needs substantial hardware.

MindsHub free tier

The MindsHub free tier includes selected models at no charge. Currently included: the MindsHub Air model and Jev. No credit card is required; fair use and daily rate limits apply.

Make the final choice on your own work

Choose representative tasks and agree on what a usable result looks like. Check correctness, required input types, tool use, response time, and data-handling terms. Then divide the total spend, including failed attempts, by the number of results you would accept.

Our cost-per-task guide explains that calculation. The model selection guide covers the evaluation and hosting decisions. If you are changing the workspace as well as the model, use the workspace migration checklist to preserve instructions, connections, and reusable work.

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